Accurately estimating forest aboveground biomass (AGB) based on remote sensing (RS) images at the regional level is challenging due to the uncertainty of the modeling sample size. In this study, a new optimizing method for the samples was suggested by integrating variance function in Geostatistics and value coefficient (VC) in Value Engineering. In order to evaluate the influence of the sample size for RS models, the random forest regression (RFR), nearest neighbor (K-NN) method, and partial least squares regression (PLSR) were conducted by combining Landsat8/OLI imagery in 2016 and 91 Pinus densata sample plots in Shangri-La City of China. The mean of the root mean square error (RMSE) of 200 random sampling tests was adopted as the accuracy evaluation index of the RS models and VC as a relative cost index of the modeling samples. The research results showed that: (1) the statistical values (mean, standard deviation, and coefficient of variation) for each group of samples based on 200 experiments were not significantly different from the sampling population (91 samples) by t-test (p = 0.01), and the sampling results were reliable for establishing RS models; (2) The reliable analysis on the RFR, K-NN, and PLSR models with sample groups showed that the VC decreases with increasing samples, and the decreasing trend of VC is consistent. The number of optimal samples for RFR, K-NN, and PLSR was 55, 54, and 56 based on the spherical model of variance function, respectively, and the optimal results were consistent. (3) Among the established models based on the optimal samples, the RFR model with the determination coefficient R² = 0.8485, RMSE = 12.25 Mg/hm², and the estimation accuracy P = 81.125% was better than K-NN and PLSR. Therefore, they could be used as models for estimating the aboveground biomass of Pinus densata in the study area. For the optimal sample size and sampling population, the RFR model of Pinus densata AGB was established, combining 26 variable factors in the study area. The total AGB with the optimal samples was 1.22 × 10⁷ Mg, and the estimation result with the sampling population was 1.24 × 10⁷ Mg based on Landsat8/OLI images. Respectively, the average AGB was 66.42 Mg/hm² and 67.51 Mg/hm², with a relative precision of 98.39%. The estimation results of the two sample groups were consistent.

Compared with the previous full-waveform data, the new generation of ICESat-2/ATLAS (Advanced Terrain Laser Altimeter System) has a larger footprint overlap density and a smaller footprint area. This study used ATLAS data to estimate forest aboveground biomass (AGB) in a high-altitude, ecologically fragile area. The paper used ATLAS data as the main information source and a typical mountainous area in Shangri-La, northwestern Yunnan Province, China, as the study area. Then, we combined biomass data from 54 ground samples to obtain the estimated AGB of 74,873 footprints using a hyperparametric optimized random forest (RF) model. The total AGB was estimated by combining the best variance function model in geostatistics with the slope that is the covariates. The results showed that among the 50 index parameters and three topographic variables extracted based on ATLAS, six variables showed a significant correlation with AGB. They were, in order, number of canopy photons, Landsat percentage canopy, canopy photon rate, slope, number of photons, and apparent surface reflectance. The optimized random forest model was used to estimate the AGB within the footprints. The model accuracy was the coefficient of determination (R²) = 0.93, the root mean square error (RMSE) = 10.13 t/hm², and the population estimation accuracy was 83.3%. The optimized model has a good estimation effect and can be used for footprint AGB estimation. The spatial structure analysis of the variance function of footprint AGB showed that the spherical model had the largest fitting accuracy (R² = 0.65, the residual sum of squares (RSS) = 2.65 × 10⁻⁴), the nugget (C₀) was 0.21, and the spatial structure ratio was 94.0%. It showed that the AGB of footprints had strong spatial correlation and could be interpolated by kriging. Finally, the slope in the topographic variables was selected as the co-interpolation variable, and cokriging spatial interpolation was performed. Furthermore, a continuous map of AGB spatial distribution was obtained, and the total AGB was 6.07 × 10⁷ t. The spatial distribution of AGB showed the same trend as the distribution of forest stock. The absolute accuracy of the estimation was 82.6%, using the statistical value of the forest resource planning and design survey as a reference. The ATLAS data can improve the accuracy of AGB estimation in mountain forests.

Accurately estimating forest canopy cover (FCC) is challenging by using traditional remote sensing images at the regional level due to the spectral saturation phenomenon. In this study, to improve the estimation accuracy, a new method of FCC wall-to-wall mapping was suggested based on ice, cloud, and land elevation satellite/advanced topographic laser altimeter system (ATLAS) data. Specifically, one dataset of FCC's observations was combined with preprocessed ATLAS data and topographic factors to build a random forest regression (RFR) model. Moreover, the Co-Kriging method was used to generate spatially explicit values that are required by the RFR from the point data of ATLAS parameters, and then the wall-to-wall mapping of the FCC was conducted. The results showed that the RFR model had an accuracy of relative root-mean-square error (rRMSE) = 0.09 with a coefficient of determination (R ² ) = 0.91. The best-fit semivariogram models between primary variables and covariates were asr and TR (Model: Gaussian model, R ² = 0.94, the residual sum of squares (RSS) = 1.73 × 10 ⁻⁶ ), landsat_perc and NDVI (Model: spherical model, R ² = 0.46, RSS = 1.58 × 10 ⁻⁴ ), and photon_rate_can and slope (Model: exponential model, R ² = 0.77, RSS = 6.45 × 10 ⁻⁴ ), respectively. FCC validation result showed that the FCC's wall-to-wall mapping was in great agreement with the dataset-2 (R ² = 0.79; rRMSE = 0.11).

There are various problems concerning the acquisition of forest digital terrain using satellite-based LiDAR (LiDAR). In this paper, a method for extracting the classified photon point cloud is summarised based on the photon point cloud acquired by ICESat-2/ATLAS strong and weak beams for forest digital terrain inversion. In this study, ATLAS was used as the data source to group canopy heights, and airborne LiDAR G-LiHT was used as the validation data to verify the effect of different canopy heights on the inversion of the understorey terrain. The results show that, when the canopy heights were not grouped, the inversion accuracy of both strong and weak ATLAS beams was higher, with an R2 greater than 0.99, an RMSE of less than 0.7 m, and an MAE of less than 0.4 m. Overall, the strong beams were superior. Moreover, when the canopy heights were grouped, the R2 was greater than 0.99 and the accuracy was best at the canopy height of 5-10 m. The R2 decreased when the height exceeded 35 m. The inversion accuracy of both the strong and weak ATLAS beams was higher than 0.99. The accuracy of both strong and weak beam inversions was extremely high, with the strong beam being superior overall. However, both strong and weak beams can provide a scientific basis for the inversion of understory DTMs. Canopy height affected the inversion accuracy, which was inversely proportional to the canopy height, with an increase in canopy height causing a decrease in the accuracy. The inversion of the understorey topography was better than that of the dwarf tree and shrub layers.